| First Name |
Last Name |
Organization |
Country |
Title |
Abstract |
| Mark |
Bailey |
JMP Statistical Discovery LLC |
United States |
Approaches to Comparisons with JMP® 17 |
It is common to need to compare two populations with only a sample of each population. Statistical inference can help the comparison. Our presentation is about inference involving two hypotheses: the alternative hypothesis and the null hypothesis. Sometimes the goal is to provide sufficient evidence to decide that there is a significant difference between two populations. The goal at other times is to provide sufficient evidence that there is significant equivalence, non-inferiority, or superiority between two populations. These two situations require different tests. We will review these situations, appropriate hypotheses, and appropriate tests using common examples. Another common comparison is between two measurements of the same quantity.Our presentation will focus instead on the Method Comparison protocol for chemical and biological assays used by pharmaceutical and biotechnology development and manufacturing. We will present two methods that are available in JMP 17 to assess the accuracy of a new test method against an established reference method. One method is known as Deming regression or Fit Orthogonal in JMP. The second method is known as Passing-Bablok regression. We will review the background of assessing accuracy, the unique nature of data from method comparisons, and demonstrate both regression methods with examples. |
| Brian |
Corcoran |
SAS |
United States |
Automatic data refresh in JMP Live 17 |
In past Discovery talks, we've shown how to acquire data, create a report and publish it to JMP Live using the desktop task scheduler. But, what if you have JMP Live and your report is not changing from day to day? Only the data changes, and you want to share those updates with your colleagues. JMP Live now provides a capability to schedule the refresh of your data on JMP Live without having to publish your work again. This talk will show how to use this new capability. We'll also discuss this new feature in the context of greater plans for the future of JMP and JMP Live. |
| Jon |
Armer |
Nuclera Nucleics |
United Kingdom |
Using JMP to REALLY understand what is important in your processes. |
A lot of scientists are sure they understand their processes, and in truth, to a certain degree they do. However it is always the edge cases that cause us problems, and this is where our understanding of our processes can often break-down. It's also, as per Murphy's law; something that tends to occur at the most inopportune moments. In this 30 minute paper I will discuss the strategies I use to identify all the levers in a process, assess and understand their importance and effect, and how to use the in-built statistics within JMP to identify the intangable factors you haven't yet considered. I'll present a case-study on an issue with a classic analytical method that led to random batch failures, how we investigated it and how we used JMP to identify the highly unlikely cause. |
| Tomasz |
Witkos |
AstraZeneca |
United Kingdom |
Speeding up assay development with automated workflow for assessing plate bias of microtiter assays. |
Understanding positional effects in microtiter plate-based assays is an important step in developing robust potency assays for testing biologics. Here, we present a bespoke JMP application that automates the analysis of multiple-plate experiments aimed at detecting plate bias. The application, using the raw output plate reader file, creates plate map visualisations, calculates IC 50 /EC 50 of drug dose-response curves and analyses row and column variabilities across plates. What typically took scientists half a day of analysis, error-prone manual formatting of datasets and need of external software for curve fitting, now can be all achieved in JMP in under 1 minute. |
| Phil |
Kay |
SAS |
United Kingdom |
Sowing the Seeds of Love for DOE |
There is a big problem with how we are educating our future scientists: we tell them that you are only allowed to change one thing in an experiment and you have to keep everything else the same. When you are trying to learn about the effect of many factors on a process or system it is much more effective to change all your factors at the same time. But people rarely learn about this method, "Design of Experiments" or "DOE". Or they only hear about it later in their careers when they are resistant to new ideas. The result is a huge waste of time and resources due to inefficient experimentation. In this poster you will learn about an engaging and simple experiment that can be used to demonstrate the power of DOE to students of all ages. You will hear how experimenters can get their hands dirty growing garden cress under different conditions according to a statistically designed experiment. And you will see how the results can be easily analysed with compelling visuals, as well as using sophisticated Functional DOE analysis in JMP Pro. |
| Peter |
Hersh |
SAS |
United States |
Want to Know What Analysis You Need? Just Ask DAD |
When analyzing data, scientists and engineers often know what they want to accomplish but are not certain which statistical test is needed. In this talk, we introduce a new add-in, the Data Analysis Director, or DAD for short. This add-in was designed to make it easier to find the proper statistical test inside of JMP, based on the analysis task, goal, and type of data you have. The add-in provides a guided flow, to help you find the right analysis and run it in JMP. It includes built-in examples, links to the JMP Help, demo videos, and even lets you launch the analysis on your own data. As you will see, DAD is a useful tool that can help guide you along your analytic journey. |
| Giulia |
Lambiase |
AstraZeneca |
United Kingdom |
DoE-Assisted Chromatography Method Development for the Analysis Biopharmaceuticals |
Cation-exchange chromatography (CEX) is the industry gold standard for the analysis of biopharmaceuticals charge variants. However, the development of CEX methods in a time and resource-efficient manner constitutes a bottleneck in product characterisation. CEX separations are complex and governed by multiple factors. Several scientific publications have proven the successful application of design-of-experiment (DoE) in chromatography method development. Nevertheless, performing DoEs with a large number of factors may be challenging, time-consuming and expensive. This work illustrates the use of a split-DoE approach to aid the development of a CEX method for the analysis of the charge variants profile of a mAb candidate. Analytical method development was intended to provide a high-throughput (HT) CEX method to support charge variants analysis with minimal sample and time requirements. The split-DoE approach is based on fundamental knowledge of the CEX separation mechanism and aims to reduce the number of experimental runs whilst exploring a wide experimental space. Regression modelling was used to study the effect of both individual process parameters and their interactions on the separation efficiency to ultimately identify the optimal method conditions. This study provides an efficient workflow for leveraging the development of CEX methods. |
